Efficient mid-query re-optimization of sub-optimal query execution plans
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Cost-based query scrambling for initial delays
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Adapting to source properties in processing data integration queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Robust query processing through progressive optimization
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Supporting top-k join queries in relational databases
The VLDB Journal — The International Journal on Very Large Data Bases
Self-monitoring query execution for adaptive query processing
Data & Knowledge Engineering
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Parallel querying with non-dedicated computers
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Content-based routing: different plans for different data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
An Adaptive Multi-Objective Scheduling Selection Framework for Continuous Query Processing
IDEAS '05 Proceedings of the 9th International Database Engineering & Application Symposium
Adaptive rank-aware query optimization in relational databases
ACM Transactions on Database Systems (TODS)
Adaptive join processing in pipelined plans
Proceedings of the 13th International Conference on Extending Database Technology
Efficient computation of search computing queries
Search computing
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In Search Computing, queries act over internet resources, and combine access to standard web services with exact results and to ranked search services. Such resources often provide limited statistical information that can be used to inform static query optimization, and correlations between the values and ranks associated with different resources may only become clear at query runtime. As a result, search computing seems likely to benefit from adaptive query processing, where information obtained during query evaluation is used to change the way in which a query is executing. This chapter provides a perspective on how run-time adaptivity can be achieved in the context of Search Computing.